[Ml-stat-talks] Fwd: [talks] Colloquium speaker Edoardo Airoldi- Tuesday, Dec 2, 4:30pm

Kosuke Imai kimai at Princeton.Edu
Mon Dec 1 14:38:55 EST 2014

Hi all,

  Edo is also speaking about a related topic in the politics department at
the Political Methodology Seminar this Friday:

This Friday, on December 5th, Professor Edoardo Airoldi (Harvard
University, Department of Statistics) will be presenting his paper titled
"Design and Analysis of Experiments in the Presence of Network
Interference" in the Political Methodology Seminar.  The paper's abstract

"A number of scientific problems involve populations with interacting
and/or interfering units. In these problems, measurements about
interactions and interference (e.g., social structure and familial
relations) are available, in addition to traditional measurements about
unit-level outcomes and covariates. Formal statistical models for the
analysis of this type of data have emerged as a major topic of interest in
diverse areas of science. In this talk, I will review a few ideas and open
research problems that are central to this burgeoning literature, placing
emphasis on inference and other core statistical issues. Then I will turn
to describe: (1) a new notion of non-ignorability that applies to network
sampling designs, (2) an inference strategy that can be used to obtain
valid estimates in these settings, and (3) a strategy to extend the Rubin
framework for estimating causal effects in the presence of social

The paper will be available later this week, and the seminar's calendar can
be found at:
http://q-aps.princeton.edu/book/political-methodology-research-seminar. We
will be meeting at noon on Friday in Corwin 127.

Kosuke Imai               Office: Corwin Hall 036
Professor                 Phone: 609-258-6601
Department of Politics    Fax: 609-258-1110
Princeton University      Email: kimai at Princeton.Edu
Princeton, NJ 08544-1012  http://imai.princeton.edu

On Mon, Dec 1, 2014 at 2:13 PM, Barbara Engelhardt <bee at cs.princeton.edu>

> Colloquium Speaker
> Edoardo Airoldi, Harvard University
> Tuesday, December 2, 4:30pm
> Computer Science 105
> Statistical and machine learning challenges in the analysis of large
> networks
> Network data --- i.e., collections of measurements on pairs, or tuples, of
> units in a population of interest --- are ubiquitous nowadays in a wide
> range of machine learning applications, from molecular biology to marketing
> on social media platforms. Surprisingly, assumptions underlying popular
> statistical methods are often untenable in the presence of network data.
> Established machine learning algorithms often break when dealing with
> combinatorial structure. And the classical notions of variability, sample
> size and ignorability take unintended connotations. These failures open to
> door to a number of technical challenges, and to opportunities for
> introducing new fundamental ideas and for developing new insights. In this
> talk, I will discuss open statistical and machine learning problems that
> arise when dealing with large networks, mostly focusing on modeling and
> inferential issues, and provide an overview of key technical ideas and
> recent results and trends.
> Edoardo M. Airoldi is an Associate Professor of Statistics at Harvard
> University, where he leads the Harvard Laboratory for Applied Statistical
> Methodology. He holds a holds Ph.D. in Computer Science and an M.Sc. in
> Statistics from Carnegie Mellon University, and a B.Sc. in Mathematical
> Statistics and Economics from Bocconi University. His current research
> focuses on statistical theory and methods for designing and analyzing
> experiments in the presence of network interference, and on inferential
> issues that arise in models of network data. He works on applications in
> molecular biology and proteomics, and in social media analytics and
> marketing. Airoldi is the recipient several research awards including the
> ONR Young Investigator Award, the NSF CAREER Award, and the Alfred P. Sloan
> Research Fellowship, and has received several outstanding paper awards
> including the Thomas R. Ten Have Award for his work on causal inference,
> and the John Van Ryzin Award for his work in biology.
>  He has recently advised the Obama for America 2012 campaign on their
> social media efforts, and serves as a technical advisor at Nanigans and
> Maxpoint.
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